class_docstr: >>> LitClassifier(Backbone())  # doctest: +ELLIPSIS +NORMALIZE_WHITESPACE
  LitClassifier(
    (backbone): ...
  )
code: def validation_step(self, batch, batch_idx):
      x, y = batch
      y_hat = self(x)
      loss = F.cross_entropy(y_hat, y)
      self.log("valid_loss", loss, on_step=True)
  
imports: from os import path
  from typing import Optional
  import torch
  from lightning.pytorch import LightningDataModule, LightningModule, cli_lightning_logo
  from lightning.pytorch.cli import LightningCLI
  from lightning.pytorch.demos.mnist_datamodule import MNIST
  from lightning.pytorch.utilities.imports import _TORCHVISION_AVAILABLE
  from torch.nn import functional
  from torch.utils.data import DataLoader, random_split
label_desc: Use image node_id 4 for calling the LitClassifier obj's underlying member method code with example usage: obj.validation_step(batch, batch_idx) without return types